[Sds-seminars] [Sds-announce] S&DS Talk by Chara Podimata, 2/23, 4pm, "Incentive-Aware Machine Learning for Decision Making"

[elizavette.torres at yale.edu]

   <https://statistics.yale.edu/> Department of Statistics and Data Science



CHARA PODIMATA, Harvard University




Wednesday, February 23, 2022

4:00PM to 5:00PM

Zoom: https://yale.zoom.us/j/94400474248

 <https://www.charapodimata.com/> Website

 

 

 

Title: Incentive-Aware Machine Learning for Decision Making

Information and Abstract: 

As machine learning algorithms are increasingly being deployed for
consequential decision making (e.g., loan approvals, college admissions,
probation decisions etc.) humans are trying to strategically change the data
they feed to these algorithms in an effort to obtain better decisions for
themselves. If the deployed algorithms do not take these incentives into
account they risk creating policy decisions that are incompatible with the
original policy's goal. 

In this talk, I will give an overview of my work on Incentive-Aware Machine
Learning for Decision Making, which studies the effects of strategic
behavior both to institutions and society as a whole and proposes ways to
robustify machine learning algorithms to strategic individuals. I will first
explain the goals of the different stakeholders (institution, individual,
society) in these settings in a unified way and show the various settings I
have worked on that belong in the incentive-aware machine learning area such
as incentive-compatible algorithms for linear regression and online
prediction with expert advice, strategic classification, learning in
auctions, and dynamic pricing. I will conclude by looking at the problem
from a societal lens and discuss the tension that arises between having
decision-making algorithms that are fully transparent and incentive-aware. 

Bio: Chara is a final year PhD student at Harvard, where she is advised by
Yiling Chen. Her research is supported by a Microsoft Dissertation Grant and
a Siebel Scholarship. During her PhD, she interned twice for MSR NYC
(mentored by Jennifer Wortman Vaughan and Aleksandrs Slivkins) and once for
Google Research NYC (mentored by Renato Paes Leme). She has given tutorials
related to strategic learning at EC20 and FAccT21. Outside of research, she
spends her time adventuring with her pup, Terra.

 

 

 

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